All other things being equal predictions of a, say, linear trend
have a greater empirical content (as understood by
Popper, 1980 [53])
than predictions of a trend that is 'only' monotonic,
because linearity implies not only a particular rank order for
parameter values but also precisely specifiable distances among
them, whereas (strict) monotonicity only refers to the rank order
without specifying any distances. From this perspective, the preference
for quantitative trends is compatible with Popper's demand to
focus on the theories and hypotheses with the greatest empirical
content possible (Popper, 1980 [53]).
Whether this demand is adequate
for the domains of psychology has already been questioned elsewhere
(Hager & Westermann, 1986 [27]).
And Popper himself has pointed
out, too, that conceptualizing theories which are more and more
precise and more likely to be falsified empirically are not conducive
to scientific progress (Popper, 1981, p. 244 [54]).

The repeated reference to Popper as a philosopher whose methodology
is continuing a matter of debate should not be interpreted
as meaning that the testing strategies discussed in the present
paper serve the better falsification of substantive statements.
My focus is on the examination of psychological hypotheses and
whether the empirical data fit them or not. If a hypothesis is
valid or 'true' the probability that it is 'confirmed'
should be high, and if it is not valid or 'false' the
probability that it is 'disconfirmed' should be high.
The lower the validity of the study, in the sense used by Cook
and Campbell (1979) [],
and the poorer the correspondence between
predictions derived from the hypothesis and the statistical methods
and tests applied, i.e., the lower the hypothesis validity (cf.
Wampold et al., 1991 [63]),
the lower the probabilities of correct
decisions concerning the psychological hypothesis will be, all
other things being equal. Furthermore, the probabilities of correct
decisions will be lowered if the derivation of (psychological
and statistical) predictions do not take the empirical content
of the hypothesis into full account, that is, if the predictions
and statistical partial hypotheses are not derived appropriately
and exhaustively. Since adequate explanations and descriptions
of psychological phenomena can only be achieved through hypotheses
and theories which have passed valid empirical tests successfully,
there is no good reason to continually try to falsify these hypotheses,
as strict falsificationists would demand. The better and more
general rule demands to plan and execute experiments in a way
that gives hypotheses a good chance to be 'confirmed'
if they are 'true' and that leads to a high probability
of 'disconfirming' them if they are 'false'.
Overall, it can be said that correct decisions are more likely
the higher the validity of the experiment (see also Westermann,
1988 [64]).
Considerations like these also seem valid in the realm
of 'applied' psychology: More is gained if one knows
that an intervention program is effective than if one knows that
it is not. Since intervention research, as one possible example,
can also be designed as examining psychological hypotheses referring
to effectiveness (see Hager, 1995 [24]),
there is no great difference
between testing hypothesis about phenomena in basic psychology
and testing hypotheses in 'applied' or technological
psychology, though hypotheses serve different aims in both realms
and their theoretical background may be quite different. In both
instances, however, predictions can and should be derived from
them which refer to the same statistical constructs and which
can be submitted to the same statistical testing strategies and
tests. If the psychological hypotheses are precise, the same holds
for the predictions, and if they are imprecise, also the predictions
are less precise.